Railway infrastructure employs many diverse systems, each of which deploy many different device types each with their own diagnostic capabilities. These capabilities are rarely the same and their methods of providing data are even less likely to be the same. There is a need however to collect, compare, and correlate this data for testing, diagnostic and maintenance purposes. Currently harmonizing the data generated and the method in which it is provide is either impossible or impractical (zone controllers have different data sets due to different guideway layouts in each zone).
Current technologies such as independent data collection for each device type are too onerous as they would require some manual effort to centralize all data for cross comparison purposes. Furthermore, an existing solutions for network data collection, such as Simple Network Management Protocol (SNMP) is impractical because a) it imposes a protocol on these devices which may not be implementable, b) the SNMP protocol is based on event notification only and hence is inadequate as a data collection system, and c) it assumes that the devices generating data have a connection to a network and Network Management System (NMS) server which is not always the case.
Even SCADA (Supervisory Control and Data Acquisition) was deemed not to be a practical solution as the data sets that could be generated could potentially cause the overhead of the SCADA protocol to make the entire system unworkable. The current solutions deployed has networked devices (VOBCs, ZCs) report any faults to a central system (ATS). This is not sufficient as only those devices currently on the network are able transmit data leaving data from non-networked devices unaccounted for, and even those devices generate data that is desirable from a testing and diagnostics point of view. Furthermore, the reliability requirements of those networked devices tend to be at odds with diagnostic capabilities (the concepts of transparent voting and data smoothing to enhance reliability by definition destroy data that is required for predictive diagnostic capabilities).